HOLY's operations run on data — and right now, that foundation is yours to scale. As the Ops Analytics & Automation Manager, you'll own the analytics foundation that operations teams depend on to make decisions they can trust: designing dbt models from the ground up, shipping dashboards that actually get used, extracting insights that challenge assumptions and drive action, and using AI tooling to keep data quality tight and eliminate repetitive work at every opportunity.
This isn't just a technical build — it takes sharp business acumen to turn operational data into the reporting and insights that move an entire ops function forward. Greenfield, high autonomy, direct impact. You'll shape what gets measured at HOLY and how leadership makes decisions.
Responsibilities:- Analytics Engineering — Business & Metric Layer Ownership: Own the build-out of HOLY's dbt Business and Reporting layers — translating raw operational data from our ERP (Odoo) and other supply chain planning solutions into clean, reliable metric models. Enforce the dbt modelling conventions and layered architecture principles and maintain model quality, documentation, and test coverage.
- Supply Chain Analytics Delivery: Lead and deliver the core supply chain analytics workstreams — inventory transparency, COGS visibility, procurement pipeline, stock movements, and supplier performance — end-to-end from dbt model to dashboards in an analytics visualization solution.
- Automation & Data Quality: Build automated sanity checks, reconciliation monitors, and data quality alerts using AI-assisted coding/automation tools and scripting — replacing manual checks with systems that surface issues before they hit reporting.
- Business Intelligence & Trend Analysis: Run independent analysis on supply chain and operational trends. Challenge numbers when they look off. Bring an operations lens to data work, not just a technical one.
- Forecasting Integration Support: Ensure BigQuery outputs meet our forecasting platform requirements — clean SKU data, reliable stock and demand signals, consistent schemas.
- AI-Native Ways of Working: Use AI tools and automation tooling as core operating tools — identify where manual analytical work can be replaced with agent-assisted workflows and build them.